2026-06-07-srinivasan26a.md

title	Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesis
section	Poster
openreview	o4fb5rSinP
abstract	We present a novel framework for robust out-of-distribution planning and control using conformal prediction (CP) and system level synthesis (SLS). Our method addresses the challenge of ensuring safety and robustness when using learned dynamics models beyond the training data distribution. We first derive high-confidence bounds on model errors using weighted conformal prediction with a learned, state-control-dependent covariance model. These bounds are then integrated into an SLS-based robust nonlinear model predictive control (RMPC) formulation, which performs constraint tightening over the prediction horizon via volume-optimized forward reachable sets. We provide theoretical guarantees on coverage and robustness under distributional drift, and analyze the impact of data density and trajectory tube size on prediction coverage. Empirically, we demonstrate our approach on nonlinear systems of increasing complexity, including a 4D car and a {12D} quadcopter, showing improved safety and reliability compared to fixed-bound and non-robust baselines, especially outside of the collected data distribution.
layout	inproceedings
series	Proceedings of Machine Learning Research
publisher	PMLR
issn	2640-3498
id	srinivasan26a
month	0
tex_title	Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesis
firstpage	412
lastpage	439
page	412-439
order	412
cycles	false
bibtex_author	Srinivasan, Anutam and Leeman, Antoine and Chou, Glen
author	given family Anutam Srinivasan given family Antoine Leeman given family Glen Chou
date	2026-06-07
address
container-title	Proceedings of The 8th Annual Learning for Dynamics and Control Conference
volume	331
genre	inproceedings
issued	date-parts 2026 6 7
pdf	https://raw.githubusercontent.com/mlresearch/v331/main/assets/srinivasan26a/srinivasan26a.pdf
extras